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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Scientist, Large-Scale Data Science and Learning - **Company:** Oak Ridge National Laboratory - **Location:** Oak Ridge, TN, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Big Data, C++ (Programming Language), Collaborative Software, Collaborative Learning, Computer Programming, Computer Engineering, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Open Source Technology, Tensorflow, Supercomputing, Reinforcement Learning, Pytorch, Large Language Models, Multi-Agent Systems, Information Technology, Free and Open-Source Software, Virtual Agents, Data Selection - **Published:** August 20, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17174075?backUrl=%2Fcareer%2F17174075%2FResearch-Scientist-Large-Scale-Data-Science-Learning-Tennessee-Oak-Ridge ## About the Role * Ph.D. in Computer Science, Computer Engineering, or a field closely related to the job duties of this position. * Demonstrated research in one or more areas of HPC or AI (e.g., large-scale training, scientific reasoning, reinforcement learning, or distributed systems). * Strong programming skills (Python, C/C++, or equivalent) and experience with ML frameworks (e.g., PyTorch). Preferred Qualifications: * Experience with large-scale experiments on HPC or cloud platforms. * Strong publication record commensurate with career stage. * Familiarity with distributed training frameworks (e.g., DeepSpeed, Megatron-LM, Ray). * Demonstrated ability to work collaboratively in multidisciplinary research teams. * Interest in developing open-source tools and contributing to community efforts. ## Description The Analytics and AI Methods at Scale (AAIMS) group in the National Center for Computational Science (NCCS) is hiring a Research Scientist to advance the frontier of AI for science, including scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You'll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate discovery across domains (materials, chemistry, climate, fusion, biology, and more). NCCS operates the Frontier exascale supercomputer and world-class data facilities. This role sits at the intersection of AI at scale and HPC, giving you access to unmatched resources to prototype new ideas, run experiments, and translate methods into scientific impact. Examples of Focus Areas: * Agentic AI for Science: Autonomous and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration. * Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated decisions. * RL & Self-Improving Models: RLHF/RLAIF, online RL, self-play, open-ended discovery, reward modeling, curriculum/active learning, data selection, iterative post-training, safety alignment and guardrails. * Foundation Models for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. * Federated & Collaborative Learning: Cross-silo training across institutions and facilities; privacy-preserving learning (secure aggregation, differential privacy, MPC/HE); personalization under heterogeneity; governance-aware data/model sharing; collaborative evaluation. Major Duties and Responsibilities: * Conduct research in AI/ML at scale, working with cutting-edge HPC resources. * Collaborate with senior researchers and domain scientists on AI methods and scientific applications. * Contribute to peer-reviewed publications, technical reports, and proposals. * Engage in collaborative software development and open-source contributions. * Present research outcomes at conferences, workshops, and internal seminars. * Contribute to a supportive, inclusive, and collaborative team culture. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Unlocking the Power of AI: Accessible Language Model Tuning for All](https://www.wearedevelopers.com/videos/951-unlocking-the-power-of-ai-accessible-language-model-tuning-for-all) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)